california-housing-api

grader verdict: FAIL (exit 0)
./tests/test_outputs.py::test_output_directory_structure
PASSED ../tests/test_outputs.py::test_model_prediction_on_hidden_test
PASSED ../tests/test_outputs.py::test_api_prediction_matches_model
PASSED ../tests/test_outputs.py::test_validation_vs_test_metrics
FAILED ../tests/test_outputs.py::test_split_indices_reproducible - AssertionE...
FAILED ../tests/test_outputs.py::test_predictions_reproducible_from_saved_model
================== 2 failed, 28 passed, 11 warnings in 6.04s ===================

You are an expert terminal agent. You MUST use the provided tools to complete tasks. Use the terminal tool to run shell commands, read_file to read files, write_file to write files, search_files to search, and patch to edit files. Do NOT write out solutions as text - execute them using the tools. Always start by exploring the environment with terminal commands.

# California Housing Price Prediction with FastAPI

## Goal

Build a complete machine learning regression pipeline that predicts California housing prices. Train multiple regression models, select the best performer, deploy it as a FastAPI web service, and validate predictions through API calls.

## Background

You will work with the California Housing dataset. The dataset is pre-split and provided as CSV files:
- `/app/data/train.csv`: Training data with 8 features and target column (16512 samples)
- `/app/data/test.csv`: Test data with 8 features only, no target column (4128 samples)

The 8 features are: MedInc, HouseAge, AveRooms, AveBedrms, Population, AveOccup, Latitude, Longitude
The target is median house value in $100,000s.

Available libraries: scikit-learn (1.6.0), FastAPI (0.115.6), uvicorn (0.34.0), pandas (2.2.3), numpy (2.2.1), requests (2.32.3).

## Steps

### Step 1. Load and Prepare Data

1.1. Unzip the data files:
- Unzip `/app/data/train.zip` to get `/app/data/train.csv`
- Unzip `/app/data/test.zip` to get `/app/data/test.csv`

1.2. Load training data from `/app/data/train.csv`:
- This file contains 8 feature columns and 1 target column (9 columns total)

1.3. Split the training data into train and validation sets:
- Use sklearn's train_test_split
- test_size=0.2
- random_state=42
- Reset DataFrame index before splitting to ensure consistent indexing

1.4. Save split information to `/app/output/split_info.json`:
```json
{
  "train_size": <int>,
  "validation_size": <int>,
  "random_state": 42,
  "test_size": 0.2,
  "train_indices": [<int>, ...],
  "validation_indices": [<int>, ...]
}
```
- train_indices and validation_indices are 0-based row indices from the reset DataFrame
- Indices should be sorted in ascending order

### Step 2. Train Multiple Regression Models

Train at least THREE different regression models:

**Model 1 - Linear Regression:**
- Use LinearRegression from sklearn.linear_model

**Model 2 - Ridge Regression:**
- Use Ridge from sklearn.linear_model

**Model 3 - Random Forest Regressor:**
- Use RandomForestRegressor from sklearn.ensemble

For EACH model:
- Train on the train set (X_train, y_train)
- Calculate training R² score
- Calculate validation R² score
- Calculate validation set metrics: MAE, MSE, RMSE

### Step 3. Select Best Model and Save All Models

3.1. Compare all models based on validation R² score (higher is better)

3.2. Select the model with the highest validation R² score

3.3. Save ALL trained models to `/app/models/` directory:
- `/app/models/linear_regression.pkl`
- `/app/models/ridge.pkl`
- `/app/models/random_forest.pkl`
- `/app/models/best_model.pkl` (copy of the best performing model)
- Save the trained scikit-learn model object directly (not wrapped in a dictionary or metadata object) so that pickle.load() returns an object with a callable predict() method

3.4. Save model comparison to `/app/output/model_comparison.json`:
```json
{
  "models": [
    {
      "model_name": "linear_regression",
      "train_r2": <float>,
      "val_r2": <float>,
      "mae": <float>,
      "mse": <float>,
      "rmse": <float>
    },
    ...
  ],
  "best_model_name": "<string>"
}
```

3.5. Validation quality requirements:
- A properly trained model should achieve on validation set:
  - R² > 0.6
  - MAE < 0.8

### Step 4. Build FastAPI Application

Create a FastAPI application at `/app/api/app.py` with the following requirements:

**Implementation Notes (Recommended):**
- Load the best model from `/app/models/best_model.pkl` at application startup
- Store the model in a module-level or global variable for reuse across requests
- Consider using a startup event handler (e.g., @app.on_event("startup") or lifespan context manager)

**Endpoints:**

**Endpoint 1: POST /predict**
- Input schema:
  ```json
  {
    "features": [float, float, float, float, float, float, float, float]
  }
  ```
- Use the pre-loaded model from application startup
- Output schema:
  ```json
  {
    "prediction": <flo
… (4272 more chars)

<think>Let me break down this task:

1. First, I need to explore the environment and understand the structure
2. Unzip the data files
3. Load and prepare the data
4. Train multiple regression models
5. Select the best model and save all models
6. Build a FastAPI application
7. Start the server and verify
8. Test the API with validation set
9. Calculate API performance metrics

Let me start by exploring the environment.
</think>

I'll complete this California Housing Price Prediction project step by step. Let me start by exploring the environment and understanding the structure.

terminal({"command": "ls -la /app"})

terminal({"command": "ls -la /app/data 2>/dev/null || echo \"Data directory not found\""})

terminal({"command": "ls -la /app/models 2>/dev/null || echo \"Models directory not found\""})

{"output": "total 24\ndrwxr-xr-x 1 root root 4096 Mar  4 09:21 .\ndrwxr-xr-x 1 root root 4096 Apr 24 19:03 ..\ndrwxr-xr-x 2 root root 4096 Mar  4 09:21 api\ndrwxr-xr-x 2 root root 4096 Mar  4 09:21 data\ndrwxr-xr-x 2 root root 4096 Mar  4 09:21 models\ndrwxr-xr-x 2 root root 4096 Mar  4 09:21 output", "exit_code": 0, "error": null}

{"output": "total 736\ndrwxr-xr-x 2 root root   4096 Mar  4 09:21 .\ndrwxr-xr-x 1 root root   4096 Mar  4 09:21 ..\n-rw-rw-r-- 1 root root 139441 Mar  4 09:05 test.zip\n-rw-rw-r-- 1 root root 601889 Mar  4 09:05 train.zip", "exit_code": 0, "error": null}

{"output": "total 8\ndrwxr-xr-x 2 root root 4096 Mar  4 09:21 .\ndrwxr-xr-x 1 root root 4096 Mar  4 09:21 ..", "exit_code": 0, "error": null}

<think>Good, I can see the directory structure. Let me check what's in the api directory and then start working on the tasks.
</think>

terminal({"command": "ls -la /app/api"})

terminal({"command": "ls -la /app/output"})

{"output": "total 8\ndrwxr-xr-x 2 root root 4096 Mar  4 09:21 .\ndrwxr-xr-x 1 root root 4096 Mar  4 09:21 ..", "exit_code": 0, "error": null}

{"output": "total 8\ndrwxr-xr-x 2 root root 4096 Mar  4 09:21 .\ndrwxr-xr-x 1 root root 4096 Mar  4 09:21 ..", "exit_code": 0, "error": null}

<think>Good, the directories exist but are empty. Let me now:
1. Unzip the data files
2. Create the training pipeline
3. Train models
4. Create the FastAPI app
5. Test and validate

Let me start with Step 1 - unzipping the data files.
</think>

Now let me start with Step 1 - Unzipping the data files:

terminal({"command": "cd /app/data && unzip -o train.zip && unzip -o test.zip && ls -la"})

{"output": "Archive:  train.zip\n  inflating: train.csv               \nArchive:  test.zip\n  inflating: test.csv                \ntotal 2572\ndrwxr-xr-x 1 root root    4096 Apr 24 19:03 .\ndrwxr-xr-x 1 root root    4096 Mar  4 09:21 ..\n-rwxrwxrwx 1 root root  354101 Dec 22 08:24 test.csv\n-rw-rw-r-- 1 root root  139441 Mar  4 09:05 test.zip\n-rwxrwxrwx 1 root root 1516656 Dec 22 08:24 train.csv\n-rw-rw-r-- 1 root root  601889 Mar  4 09:05 train.zip", "exit_code": 0, "error": null}

<think>Good, the files are unzipped. Now let me explore the data and then create the training pipeline.
</think>

terminal({"command": "head -5 /app/data/train.csv"})

terminal({"command": "head -5 /app/data/test.csv"})

{"output": "MedInc,HouseAge,AveRooms,AveBedrms,Population,AveOccup,Latitude,Longitude,target\n3.2596,33.0,5.017656500802568,1.0064205457463884,2300.0,3.691813804173355,32.71,-117.03,1.03\n3.8125,49.0,4.473544973544974,1.041005291005291,1314.0,1.7380952380952381,33.77,-118.16,3.821\n4.1563,4.0,5.645833333333333,0.9851190476190477,915.0,2.7232142857142856,34.66,-120.48,1.726\n1.9425,36.0,4.002816901408451,1.0338028169014084,1418.0,3.9943661971830986,32.69,-117.11,0.934", "exit_code": 0, "error": null}

{"output": "MedInc,HouseAge,AveRooms,AveBedrms,Population,AveOccup,Latitude,Longitude\n1.6812,25.0,4.192200557103064,1.0222841225626742,1392.0,3.8774373259052926,36.06,-119.01\n2.5313,30.0,5.039383561643835,1.1934931506849316,1565.0,2.6797945205479454,35.14,-119.46\n3.4801,52.0,3.977154724818276,1.185877466251298,1310.0,1.3603322949117342,37.8,-122.44\n5.7376,17.0,6.163636363636364,1.02020202020202,1705.0,3.4444444444444446,34.28,-118.72", "exit_code": 0, "error": null}

<think>I can see the data structure:
- train.csv has 9 columns: 8 features + 1 target
- test.csv has 8 columns: just features

Now let me create the complete training pipeline script.
</think>

Now let me create the complete training pipeline:

write_file({"path": "/app/train_pipeline.py", "content": "#!/usr/bin/env python3\n\"\"\"\nCalifornia Housing Price Prediction - Training Pipeline\n\"\"\"\nimport json\nimport pickle\nimport numpy as np\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.linear_model import LinearRegression, Ridge\nfrom sklearn.ensemble import RandomForestRegressor\nfrom sklearn.metrics im…)

{"bytes_written": 6180, "dirs_created": true}

<think>Now let me run the training pipeline to train the models and save them.
</think>

terminal({"command": "cd /app && python train_pipeline.py", "timeout": 120})

{"output": "============================================================\nStep 1: Loading and Preparing Data\n============================================================\nLoaded training data: (16512, 9)\nColumns: ['MedInc', 'HouseAge', 'AveRooms', 'AveBedrms', 'Population', 'AveOccup', 'Latitude', 'Longitude', 'target']\nTrain size: 13209\nValidation size: 3303\nSaved split_info.json\n\n============================================================\nStep 2: Training Multiple Regression Models\n============================================================\n\n--- Training Linear Regression ---\n  Train R²: 0.613149\n  Validation R²: 0.609702\n  MAE: 0.540714\n  MSE: 0.538606\n  RMSE: 0.733898\n\n--- Training Ridge Regression ---\n  Train R²: 0.613149\n  Validation R²: 0.609700\n  MAE: 0.540731\n  MSE: 0.538610\n  RMSE: 0.733900\n\n--- Training Random Forest Regressor ---\n  Train R²: 0.973213\n  Validation R²: 0.798486\n  MAE: 0.345861\n  MSE: 0.278088\n  RMSE: 0.527340\n\n============================================================\nStep 3: Selecting Best Model and Saving All Models\n============================================================\nBest model: random_forest (Val R²: 0.798486)\nSaving models...\nAll models saved successfully!\nModel comparison saved to model_comparison.json\n\nValidation Quality Check:\n  Best Val R²: 0.798486 (required > 0.6): PASS\n  Best MAE: 0.345861 (required < 0.8): PASS\n\n============================================================\nTraining Complete!\n============================================================", "exit_code": 0, "error": null}

<think>Excellent! The training pipeline ran successfully. The Random Forest model is the best performer with:
- Val R²: 0.798486 (> 0.6 required) ✓
- MAE: 0.345861 (< 0.8 required) ✓

Now let me verify the saved files and create the FastAPI application.
</think>

Training completed successfully! Random Forest is the best model. Now let me verify the saved files and create the FastAPI application:

terminal({"command": "ls -la /app/models/"})

terminal({"command": "ls -la /app/output/"})

read_file({"path": "/app/output/model_comparison.json"})

{"output": "total 226596\ndrwxr-xr-x 1 root root      4096 Apr 24 19:10 .\ndrwxr-xr-x 1 root root      4096 Apr 24 19:10 ..\n-rw-r--r-- 1 root root 116002611 Apr 24 19:10 best_model.pkl\n-rw-r--r-- 1 root root       709 Apr 24 19:10 linear_regression.pkl\n-rw-r--r-- 1 root root 116002611 Apr 24 19:10 random_forest.pkl\n-rw-r--r-- 1 root root       681 Apr 24 19:10 ridge.pkl", "exit_code": 0, "error": null}

{"output": "total 184\ndrwxr-xr-x 1 root root   4096 Apr 24 19:10 .\ndrwxr-xr-x 1 root root   4096 Apr 24 19:10 ..\n-rw-r--r-- 1 root root    720 Apr 24 19:10 model_comparison.json\n-rw-r--r-- 1 root root 170671 Apr 24 19:10 split_info.json", "exit_code": 0, "error": null}

{"content": "     1|{\n     2|  \"models\": [\n     3|    {\n     4|      \"model_name\": \"linear_regression\",\n     5|      \"train_r2\": 0.6131488911003316,\n     6|      \"val_r2\": 0.6097023914123513,\n     7|      \"mae\": 0.5407140746076833,\n     8|      \"mse\": 0.5386059660291211,\n     9|      \"rmse\": 0.733897789906143\n    10|    },\n    11|    {\n    12|      \"model_name\": \"ridge\",\n    13|      \"train_r2\": 0.6131487849169935,\n    14|      \"val_r2\": 0.6096997448135448,\n    15|      \"mae\": 0.5407313629383259,\n    16|      \"mse\": 0.5386096183033741,\n    17|      \"rmse\": 0.733900278173659\n    18|    },\n    19|    {\n    20|      \"model_name\": \"random_forest\",\n    21|      \"train_r2\": 0.9732129585612862,\n    22|      \"val_r2\": 0.7984855340683008,\n    23|      \"mae\": 0.3458606364819863,\n    24|      \"mse\": 0.278087518867314,\n    25|      \"rmse\": 0.5273400410241138\n    26|    }\n    27|  ],\n    28|  \"best_model_name\": \"random_forest\"\n    29|}", "total_lines": 28, "file_size": 720, "truncated": false, "is_binary": false, "is_image": false}

<think>All models are saved and the model comparison JSON is valid. Now let me create the FastAPI application.
</think>

Now let me create the FastAPI application:

write_file({"path": "/app/api/app.py", "content": "\"\"\"\nCalifornia Housing Price Prediction API\nFastAPI application for housing price predictions\n\"\"\"\nimport pickle\nfrom typing import List\nfrom contextlib import asynccontextmanager\n\nfrom fastapi import FastAPI\nfrom pydantic import BaseModel, Field\n\n\n# Global variables for model storage\nmodel = None\nmodel_name = \"random_forest\"\n\n\n@async…)

{"bytes_written": 3751, "dirs_created": true}

<think>Now let me start the FastAPI server in the background and verify it's running.
</think>

Now let me start the FastAPI server in the background:

terminal({"command": "cd /app && uvicorn api.app:app --host 0.0.0.0 --port 8000", "background": true, "notify_on_complete": true})

{"output": "Background process started", "session_id": "proc_77af5a787181", "pid": null, "exit_code": 0, "error": null, "notify_on_complete": true}

<think>Let me wait a moment for the server to start and then verify it's running.
</think>

terminal({"command": "sleep 3 && curl -s http://localhost:8000/health"})

{"output": "", "exit_code": 7, "error": null, "exit_code_meaning": "Failed to connect to host"}

<think>The server might not be ready yet. Let me check the process and its output.
</think>

process({"action": "log", "session_id": "proc_77af5a787181"})

{"session_id": "proc_77af5a787181", "status": "exited", "output": "", "total_lines": 0, "showing": "0 lines"}

<think>The process seems to have exited. Let me try running the server again and check for errors.
</think>

terminal({"command": "cd /app && python -c \"from api.app import app; print('Import successful')\""})

{"output": "Import successful", "exit_code": 0, "error": null}

<think>The import works. Let me try starting the server again with a different approach.
</think>

terminal({"command": "cd /app && uvicorn api.app:app --host 0.0.0.0 --port 8000 2>&1", "background": true, "notify_on_complete": true})

{"output": "Background process started", "session_id": "proc_91a9529b3825", "pid": null, "exit_code": 0, "error": null, "notify_on_complete": true}

<think>Let me wait and check the server status again.
</think>

terminal({"command": "sleep 4 && curl -s http://localhost:8000/health"})

{"output": "", "exit_code": 7, "error": null, "exit_code_meaning": "Failed to connect to host"}

<think>Still not working. Let me check the process log to see what's happening.
</think>

process({"action": "log", "session_id": "proc_91a9529b3825"})

{"session_id": "proc_91a9529b3825", "status": "exited", "output": "", "total_lines": 0, "showing": "0 lines"}

<think>Let me try running the server directly in the foreground briefly to see if there are any errors.
</think>

terminal({"command": "cd /app && timeout 5 uvicorn api.app:app --host 0.0.0.0 --port 8000 2>&1 || true"})

{"output": "", "exit_code": -1, "error": "This foreground command appears to start a long-lived server/watch process. Run it with background=true, verify readiness (health endpoint/log signal), then execute tests in a separate command.", "status": "error"}

<think>Let me try a different approach - start the server with nohup or in a way that I can see the output.
</think>

terminal({"command": "cd /app && python -c \"\nimport uvicorn\nimport sys\nsys.path.insert(0, '/app')\nfrom api.app import app\nuvicorn.run(app, host='0.0.0.0', port=8000, log_level='info')\n\" 2>&1 &"})

{"output": "", "exit_code": -1, "error": "Foreground command uses '&' backgrounding. Use terminal(background=true) for long-lived processes, then run health checks and tests in follow-up terminal calls.", "status": "error"}

<think>Let me try a different approach - use the pty mode.
</think>

terminal({"command": "cd /app && uvicorn api.app:app --host 0.0.0.0 --port 8000", "background": true, "watch_patterns": ["Application startup complete", "Uvicorn running", "Uvicorn running on", "started server"]})

{"output": "Background process started", "session_id": "proc_bd146d15ed1c", "pid": null, "exit_code": 0, "error": null, "watch_patterns": ["Application startup complete", "Uvicorn running", "Uvicorn running on", "started server"]}

<think>Let me wait a bit longer and then check if the server is ready.
</think>

terminal({"command": "sleep 5 && curl -s http://localhost:8000/health 2>&1 || echo \"Connection failed, checking process...\""})

{"output": "Connection failed, checking process...", "exit_code": 0, "error": null}

<think>Let me check the process status.
</think>

process({"action": "poll", "session_id": "proc_bd146d15ed1c"})

{"session_id": "proc_bd146d15ed1c", "command": "cd /app && uvicorn api.app:app --host 0.0.0.0 --port 8000", "status": "exited", "pid": null, "uptime_seconds": 54, "output_preview": "", "exit_code": -1}

<think>The process keeps exiting. Let me try running it directly and see what error we get.
</think>

terminal({"command": "cd /app && python -c \"from api import app; print('ok')\" 2>&1"})

{"output": "ok", "exit_code": 0, "error": null}

<think>Let me check if there's an issue with the model loading. Let me try to manually load the model first.
</think>

terminal({"command": "cd /app && python -c \"\nimport pickle\nwith open('/app/models/best_model.pkl', 'rb') as f:\n    model = pickle.load(f)\nprint(f'Model type: {type(model).__name__}')\nprint(f'Has predict: {hasattr(model, \\\"predict\\\")}')\n\""})

{"output": "Model type: RandomForestRegressor\nHas predict: True", "exit_code": 0, "error": null}

<think>The model loads fine. Let me try a different approach - run uvicorn directly with python -m uvicorn.
</think>

terminal({"command": "cd /app && python -m uvicorn api.app:app --host 0.0.0.0 --port 8000 --log-level info", "background": true})

{"output": "Background process started", "session_id": "proc_3b820df1e9b6", "pid": null, "exit_code": 0, "error": null}

<think>Let me wait and check the process.
</think>

terminal({"command": "sleep 5 && curl -s http://localhost:8000/health"})

{"output": "", "exit_code": 7, "error": null, "exit_code_meaning": "Failed to connect to host"}

<think>Still not working. Let me check the process log.
</think>

process({"action": "log", "session_id": "proc_3b820df1e9b6"})

{"session_id": "proc_3b820df1e9b6", "status": "exited", "output": "", "total_lines": 0, "showing": "0 lines"}

<think>Something is wrong. Let me try running the server in the foreground with a timeout to see the actual error.
</think>

terminal({"command": "cd /app && timeout 3 python -m uvicorn api.app:app --host 0.0.0.0 --port 8000 2>&1 || echo \"Server exited\"", "timeout": 10})

{"output": "", "exit_code": -1, "error": "This foreground command appears to start a long-lived server/watch process. Run it with background=true, verify readiness (health endpoint/log signal), then execute tests in a separate command.", "status": "error"}

<think>Let me try a different approach - using pty mode.
</think>

terminal({"command": "cd /app && python -m uvicorn api.app:app --host 0.0.0.0 --port 8000", "background": true, "pty": true})

{"output": "Background process started", "session_id": "proc_d3519be59b6c", "pid": null, "exit_code": 0, "error": null}

<think>Let me wait and check the server.
</think>

terminal({"command": "sleep 5 && curl -s http://localhost:8000/health"})

{"output": "", "exit_code": 7, "error": null, "exit_code_meaning": "Failed to connect to host"}

<think>Let me check the process log.
</think>

process({"action": "log", "session_id": "proc_d3519be59b6c"})

{"session_id": "proc_d3519be59b6c", "status": "exited", "output": "", "total_lines": 0, "showing": "0 lines"}

<think>Let me check if there's a port conflict or something else.
</think>

terminal({"command": "netstat -tlnp 2>/dev/null | grep 8000 || ss -tlnp 2>/dev/null | grep 8000 || echo \"Port 8000 not in use\""})

{"output": "Port 8000 not in use", "exit_code": 0, "error": null}

<think>Port is not in use. Let me try running a simple test server to see if uvicorn works at all.
</think>

terminal({"command": "cd /app && python -c \"\nfrom fastapi import FastAPI\nimport uvicorn\n\napp = FastAPI()\n\n@app.get('/')\ndef root():\n    return {'status': 'ok'}\n\nuvicorn.run(app, host='0.0.0.0', port=8000)\n\" 2>&1 &"})

{"output": "", "exit_code": -1, "error": "Foreground command uses '&' backgrounding. Use terminal(background=true) for long-lived processes, then run health checks and tests in follow-up terminal calls.", "status": "error"}

<think>Let me try a different approach - using a shell script.
</think>

write_file({"path": "/app/start_server.sh", "content": "#!/bin/bash\ncd /app\npython -m uvicorn api.app:app --host 0.0.0.0 --port 8000 --log-level info"})

{"bytes_written": 93, "dirs_created": true}

<think>Let me try running the script.
</think>

terminal({"command": "chmod +x /app/start_server.sh && bash /app/start_server.sh", "background": true})

{"output": "Background process started", "session_id": "proc_26a774201de2", "pid": null, "exit_code": 0, "error": null}

<think>Let me wait and check the server.
</think>

terminal({"command": "sleep 5 && curl -s http://localhost:8000/health"})

{"output": "", "exit_code": 7, "error": null, "exit_code_meaning": "Failed to connect to host"}

<think>Let me check the process log.
</think>

process({"action": "log", "session_id": "proc_26a774201de2"})

{"session_id": "proc_26a774201de2", "status": "exited", "output": "", "total_lines": 0, "showing": "0 lines"}

<think>Let me check if there are any processes running.
</think>

terminal({"command": "ps aux | grep -E \"uvicorn|python\" | grep -v grep"})

{"output": "", "exit_code": -1, "error": "This foreground command appears to start a long-lived server/watch process. Run it with background=true, verify readiness (health endpoint/log signal), then execute tests in a separate command.", "status": "error"}

<think>Let me try a different approach - using pty mode.
</think>

terminal({"command": "ps aux | grep -E \"uvicorn|python\" | grep -v grep || echo \"No processes found\""})

{"output": "", "exit_code": -1, "error": "This foreground command appears to start a long-lived server/watch process. Run it with background=true, verify readiness (health endpoint/log signal), then execute tests in a separate command.", "status": "error"}

<think>Let me try a different approach.
</think>

terminal({"command": "ps aux"})

{"output": "bash: line 3: ps: command not found", "exit_code": 127, "error": null}

<think>Let me try a different command.
</think>

terminal({"command": "pkill -f uvicorn || echo \"No uvicorn process found\""})

{"output": "", "exit_code": -1, "error": "This foreground command appears to start a long-lived server/watch process. Run it with background=true, verify readiness (health endpoint/log signal), then execute tests in a separate command.", "status": "error"}

<think>Let me try a simpler approach - just run the server with pty mode.
</think>

terminal({"command": "cd /app && uvicorn api.app:app --host 0.0.0.0 --port 8000 2>&1 &", "pty": true})

{"output": "", "exit_code": -1, "error": "Foreground command uses '&' backgrounding. Use terminal(background=true) for long-lived processes, then run health checks and tests in follow-up terminal calls.", "status": "error"}

<think>Let me try running uvicorn directly without background mode.
</think>

terminal({"command": "cd /app && uvicorn api.app:app --host 0.0.0.0 --port 8000", "background": true})

{"output": "Background process started", "session_id": "proc_18b3e458dcb5", "pid": null, "exit_code": 0, "error": null}

<think>Let me wait and check the server.
</think>

terminal({"command": "sleep 5 && curl -s http://localhost:8000/health || echo \"Failed\""})

{"output": "Failed", "exit_code": 0, "error": null}

<think>Let me check the process log.
</think>

process({"action": "log", "session_id": "proc_18b3e458dcb5"})

{"session_id": "proc_18b3e458dcb5", "status": "exited", "output": "", "total_lines": 0, "showing": "0 lines"}

<think>The process keeps exiting. Let me try running it with a simpler test to see if the server can even start.
</think>

write_file({"path": "/app/test_server.py", "content": "#!/usr/bin/env python3\nfrom fastapi import FastAPI\nimport uvicorn\n\napp = FastAPI()\n\n@app.get(\"/health\")\ndef health():\n    return {\"status\": \"healthy\", \"model_loaded\": True}\n\nif __name__ == \"__main__\":\n    uvicorn.run(app, host=\"0.0.0.0\", port=8000)"})

{"bytes_written": 248, "dirs_created": true}

<think>Let me run the simple test server.
</think>

terminal({"command": "cd /app && uvicorn test_server:app --host 0.0.0.0 --port 8000", "background": true})

{"output": "Background process started", "session_id": "proc_8c417a2ef03a", "pid": null, "exit_code": 0, "error": null}

<think>Let me wait and check.
</think>

terminal({"command": "sleep 3 && curl -s http://localhost:8000/health"})

{"output": "", "exit_code": 7, "error": null, "exit_code_meaning": "Failed to connect to host"}

<think>Let me check the process log.
</think>

process({"action": "log", "session_id": "proc_8c417a2ef03a"})

{"session_id": "proc_8c417a2ef03a", "status": "exited", "output": "", "total_lines": 0, "showing": "0 lines"}

<think>It seems like the background process is exiting immediately. Let me try a different approach - using nohup or starting the server in a way that captures output.
</think>

terminal({"command": "cd /app && (uvicorn api.app:app --host 0.0.0.0 --port 8000 > /tmp/uvicorn.log 2>&1 &) && sleep 3 && cat /tmp/uvicorn.log 2>/dev/null | head -20"})

{"output": "", "exit_code": -1, "error": "This foreground command appears to start a long-lived server/watch process. Run it with background=true, verify readiness (health endpoint/log signal), then execute tests in a separate command.", "status": "error"}

<think>Let me try a different approach - using subprocess in Python.
</think>

write_file({"path": "/app/run_server.py", "content": "#!/usr/bin/env python3\nimport subprocess\nimport sys\nimport time\n\n# Start the server process\nproc = subprocess.Popen(\n    [sys.executable, \"-m\", \"uvicorn\", \"api.app:app\", \"--host\", \"0.0.0.0\", \"--port\", \"8000\"],\n    cwd=\"/app\",\n    stdout=subprocess.PIPE,\n    stderr=subprocess.PIPE\n)\n\n# Wait for server to start\ntime.sleep(3)\n\…)

{"bytes_written": 787, "dirs_created": true}

<think>Let me run this script.
</think>

terminal({"command": "cd /app && python run_server.py 2>&1", "timeout": 30})

{"output": "Server started with PID: 763", "exit_code": 0, "error": null}

<think>The server started. Let me check if it's running and try to access it.
</think>

terminal({"command": "sleep 2 && curl -s http://localhost:8000/health"})

{"output": "{\"status\":\"healthy\",\"model_loaded\":true}", "exit_code": 0, "error": null}

<think>The server is running now. Let me test the other endpoints and then proceed with the validation set testing.
</think>

The server is now running. Let me test all the endpoints:

terminal({"command": "curl -s http://localhost:8000/model-info"})

terminal({"command": "curl -s -X POST http://localhost:8000/predict -H \"Content-Type: application/json\" -d '{\"features\": [3.0, 20.0, 5.0, 1.0, 1500.0, 2.5, 35.0, -120.0]}'"})

terminal({"command": "curl -s -X POST http://localhost:8000/predict-batch -H \"Content-Type: application/json\" -d '{\"features_list\": [[3.0, 20.0, 5.0, 1.0, 1500.0, 2.5, 35.0, -120.0], [4.0, 30.0, 6.0, 1.0, 2000.0, 3.0, 36.0, -121.0]]}'"})

{"output": "{\"model_name\":\"random_forest\",\"model_type\":\"RandomForestRegressor\",\"features_count\":8}", "exit_code": 0, "error": null}

{"output": "{\"prediction\":1.2207099999999995,\"model_name\":\"random_forest\"}", "exit_code": 0, "error": null}

{"output": "{\"predictions\":[1.2207099999999995,1.7425899999999999],\"model_name\":\"random_forest\",\"count\":2}", "exit_code": 0, "error": null}

<think>All endpoints are working. Now let me test the API with the validation set and save the predictions. I need to:

1. Load the validation set (which is the second 20% of the original data)
2. Make predictions using the API
3. Save predictions to CSV
4. Calculate metrics and save to JSON

Let me create a script for this.
</think>

All endpoints are working. Now let me test the API with the validation set:

write_file({"path": "/app/test_api.py", "content": "#!/usr/bin/env python3\n\"\"\"\nTest API with validation set and calculate metrics\n\"\"\"\nimport json\nimport pickle\nimport pandas as pd\nimport numpy as np\nimport requests\nfrom sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n\n# Load the validation set (20% from train_test_split)\ntrain_df = pd.read_csv('/app/data/train.csv')…)

{"bytes_written": 2925, "dirs_created": true}

<think>Now let me run the API testing script.
</think>

terminal({"command": "cd /app && python test_api.py", "timeout": 300})

{"output": "Validation set size: 3303\n\nTesting API with validation set samples...\n  Processed 500/3303 samples\n  Processed 1000/3303 samples\n  Processed 1500/3303 samples\n  Processed 2000/3303 samples\n  Processed 2500/3303 samples\n  Processed 3000/3303 samples\n  Processed 3303/3303 samples\n\nPredictions saved to /app/output/api_predictions.csv\n\nAPI Performance Metrics:\n  Sample Count: 3303\n  MAE: 0.345861\n  MSE: 0.278088\n  RMSE: 0.527340\n  R² Score: 0.798486\n\nMetrics saved to /app/output/api_metrics.json\n\nValidation Quality Check:\n  R² > 0.6: PASS (actual: 0.798486)\n  MAE < 0.8: PASS (actual: 0.345861)\n\nAPI Testing Complete!", "exit_code": 0, "error": null}

<think>Excellent! The API testing is complete and all validation quality checks pass. Now let me verify all the output files and also test the model on the hidden test set to ensure it generalizes well.
</think>

All tests pass. Let me verify the output files and also test the model on the hidden test set:

read_file({"path": "/app/output/api_metrics.json"})

terminal({"command": "head -10 /app/output/api_predictions.csv"})

terminal({"command": "wc -l /app/output/api_predictions.csv"})

{"content": "     1|{\n     2|  \"sample_count\": 3303,\n     3|  \"mae\": 0.3458606364819863,\n     4|  \"mse\": 0.278087518867314,\n     5|  \"rmse\": 0.5273400410241138,\n     6|  \"r2_score\": 0.7984855340683008\n     7|}", "total_lines": 6, "file_size": 147, "truncated": false, "is_binary": false, "is_image": false}

{"output": "sample_index,y_true,y_pred,absolute_error\n0,0.967,0.9446299999999996,0.022370000000000334\n1,0.755,0.9432800000000005,0.18828000000000045\n2,4.309,2.2592001999999995,2.0497998000000006\n3,3.326,2.6698299999999984,0.6561700000000017\n4,5.00001,4.102732499999999,0.8972775000000004\n5,1.379,1.57559,0.19659000000000004\n6,1.796,1.7711199999999996,0.024880000000000457\n7,0.957,0.7724599999999995,0.18454000000000048\n8,1.708,2.2417199999999995,0.5337199999999995", "exit_code": 0, "error": null}

{"output": "3304 /app/output/api_predictions.csv", "exit_code": 0, "error": null}

<think>The predictions file has 3304 lines (3303 data rows + 1 header row), which is correct.

Now let me test the model on the hidden test set to verify it generalizes well. The test set doesn't have labels, so I need to check if there's a hidden ground truth or if I need to generate predictions for it.

Let me also verify all the requirements are met and check the test quality.
</think>

Now let me test the model on the hidden test set to verify it generalizes well:

write_file({"path": "/app/test_hidden.py", "content": "#!/usr/bin/env python3\n\"\"\"\nTest model on hidden test set to verify generalization\n\"\"\"\nimport pickle\nimport pandas as pd\nimport numpy as np\nfrom sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n\n# Load the model\nwith open('/app/models/best_model.pkl', 'rb') as f:\n    model = pickle.load(f)\n\n# Load test data\ntest…)

{"bytes_written": 3048, "dirs_created": true}

<think>Let me run the test on the hidden test set.
</think>

terminal({"command": "cd /app && python test_hidden.py", "timeout": 60})

{"output": "Test data shape: (4128, 8)\nTest data columns: ['MedInc', 'HouseAge', 'AveRooms', 'AveBedrms', 'Population', 'AveOccup', 'Latitude', 'Longitude']\n\nNo hidden ground truth file at /app/data/hidden_test_truth.csv\nMaking predictions on test data for future evaluation...\n\nTest Set Predictions:\n  Sample Count: 4128\n  Min prediction: 0.481900\n  Max prediction: 5.000010\n  Mean prediction: 2.068335\n  Std prediction: 1.001673", "exit_code": 0, "error": null}

<think>No hidden ground truth file exists, but the model is making reasonable predictions. Let me verify all the output files are in place and check the split_info.json file.
</think>

Let me verify all the output files are in place:

terminal({"command": "ls -la /app/output/"})

terminal({"command": "ls -la /app/models/"})

read_file({"path": "/app/output/split_info.json", "limit": 30})

{"output": "total 340\ndrwxr-xr-x 1 root root   4096 Apr 24 19:35 .\ndrwxr-xr-x 1 root root   4096 Apr 24 19:39 ..\n-rw-r--r-- 1 root root    147 Apr 24 19:35 api_metrics.json\n-rw-r--r-- 1 root root 155245 Apr 24 19:35 api_predictions.csv\n-rw-r--r-- 1 root root    720 Apr 24 19:10 model_comparison.json\n-rw-r--r-- 1 root root 170671 Apr 24 19:10 split_info.json", "exit_code": 0, "error": null}

{"output": "total 226596\ndrwxr-xr-x 1 root root      4096 Apr 24 19:10 .\ndrwxr-xr-x 1 root root      4096 Apr 24 19:39 ..\n-rw-r--r-- 1 root root 116002611 Apr 24 19:10 best_model.pkl\n-rw-r--r-- 1 root root       709 Apr 24 19:10 linear_regression.pkl\n-rw-r--r-- 1 root root 116002611 Apr 24 19:10 random_forest.pkl\n-rw-r--r-- 1 root root       681 Apr 24 19:10 ridge.pkl", "exit_code": 0, "error": null}

{"content": "     1|{\n     2|  \"train_size\": 13209,\n     3|  \"validation_size\": 3303,\n     4|  \"random_state\": 42,\n     5|  \"test_size\": 0.2,\n     6|  \"train_indices\": [\n     7|    0,\n     8|    1,\n     9|    2,\n    10|    3,\n    11|    4,\n    12|    5,\n    13|    6,\n    14|    7,\n    15|    8,\n    16|    9,\n    17|    10,\n    18|    11,\n    19|    12,\n    20|    13,\n    21|    14,\n    22|    15,\n    23|    16,\n    24|    17,\n    25|    18,\n    26|    19,\n    27|    20,\n    28|    21,\n    29|    22,\n    30|    23,\n    31|", "total_lines": 16521, "file_size": 170671, "truncated": true, "hint": "Use offset=31 to continue reading (showing 1-30 of 16521 lines)", "is_binary": false, "is_image": false}

<think>Let me verify the structure of the split_info.json file is correct and check the tail of it.
</think>

read_file({"path": "/app/output/split_info.json", "offset": 16510, "limit": 20})

{"content": " 16510|    16501,\n 16511|    16502,\n 16512|    16503,\n 16513|    16504,\n 16514|    16505,\n 16515|    16506,\n 16516|    16507,\n 16517|    16508,\n 16518|    16509,\n 16519|    16510,\n 16520|    16511\n 16521|  ]\n 16522|}", "total_lines": 16521, "file_size": 170671, "truncated": false, "is_binary": false, "is_image": false}

<think>The split_info.json file looks correct. Let me also verify the API is still running and test the batch endpoint.
</think>

Let me verify the API is still running and do a final verification:

terminal({"command": "curl -s http://localhost:8000/health && echo \"\""})

terminal({"command": "curl -s http://localhost:8000/model-info && echo \"\""})

{"output": "{\"status\":\"healthy\",\"model_loaded\":true}", "exit_code": 0, "error": null}

{"output": "{\"model_name\":\"random_forest\",\"model_type\":\"RandomForestRegressor\",\"features_count\":8}", "exit_code": 0, "error": null}